Use AutoAI to predict time series anomalies by ibm-watson-machine-learning

This notebook contains the steps and code to demonstrate support of AutoAI experiments for timeseries data sets in Watson Machine Learning service. It introduces commands for data retrieval, training experiments, persisting pipelines, testing pipelines, deploying pipelines, and scoring.

Some familiarity with Python is helpful. This notebook uses Python 3.10.

Learning goals

The learning goals of this notebook are:

• Define Watson Machine Learning experiment for timeseries data sets.
• Work with experiments to train AutoAI TimeSeries Anomaly Prediction models.
• Compare trained models quality and select the best one for further deployment.
• Online deployment and score the trained model.

Contents

This notebook contains the following parts:

1. Setup
2. Timeseries data sets
3. Optimizer definition
4. Experiment Run
5. Pipelines comparison
6. Deploy and Score
7. Clean up
8. Summary and next steps

1. Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

• Create a Watson Machine Learning (WML) Service instance (a free plan is offered and information about how to create the instance can be found here).
• Create a Cloud Object Storage (COS) instance (a lite plan is offered and information about how to order storage can be found here).
  Note: When using Watson Studio, you already have a COS instance associated with the project you are running the notebook in.

Connection to WML

Authenticate the Watson Machine Learning service on IBM Cloud. You need to provide Cloud API key and location.

Tip: Your Cloud API key can be generated by going to the Users section of the Cloud console. From that page, click your name, scroll down to the API Keys section, and click Create an IBM Cloud API key. Give your key a name and click Create, then copy the created key and paste it below. You can also get a service specific url by going to the Endpoint URLs section of the Watson Machine Learning docs. You can check your instance location in your Watson Machine Learning (WML) Service instance details.

You can use IBM Cloud CLI to retrieve the instance location.

ibmcloud login --apikey API_KEY -a https://cloud.ibm.com
ibmcloud resource service-instance WML_INSTANCE_NAME

NOTE: You can also get a service specific apikey by going to the Service IDs section of the Cloud Console. From that page, click Create, and then copy the created key and paste it in the following cell.

Action: Enter your api_key and location in the following cell.

api_key = 'PUT_YOUR_KEY_HERE'
location = 'PASTE YOUR INSTANCE LOCATION HERE'

wml_credentials = {
    "apikey": api_key,
    "url": 'https://' + location + '.ml.cloud.ibm.com'
}

Install and import the ibm-watson-machine-learning and dependecies

Note: ibm-watson-machine-learning documentation can be found here.

!pip install -U ibm-watson-machine-learning | tail -n 1
!pip install wget | tail -n 1
!pip install plotly | tail -n 1

Successfully installed ibm-watson-machine-learning-1.0.292
Successfully installed wget-3.2
Requirement already satisfied: tenacity>=6.2.0 in /opt/conda/envs/Python-3.10/lib/python3.10/site-packages (from plotly) (8.0.1)

from ibm_watson_machine_learning import APIClient

client = APIClient(wml_credentials)

Working with spaces

You need to create a space that will be used for your work. If you do not have a space, you can use Deployment Spaces Dashboard to create one.

• Click New Deployment Space
• Create an empty space
• Select Cloud Object Storage
• Select Watson Machine Learning instance and press Create
• Copy space_id and paste it below

Tip: You can also use SDK to prepare the space for your work. More information can be found here.

Action: assign space ID below

space_id = 'PASTE YOUR SPACE ID HERE'

You can use the list method to print all existing spaces.

client.spaces.list(limit=10)

To be able to interact with all resources available in Watson Machine Learning, you need to set the space which you will be using.

client.set.default_space(space_id)

'SUCCESS'

2. Timeseries data set

Connections to COS

In next cell we read the COS credentials from the space.

cos_credentials = client.spaces.get_details(space_id=space_id)['entity']['storage']['properties']

Training data sets

Download training data from git repository and upload it to a COS.
This example uses the Electricity_daily.

datasource_name = 'bluemixcloudobjectstorage'
bucketname = cos_credentials['bucket_name']

import os, wget, zipfile

filename = 'electricity_usage.csv'
base_url = 'https://raw.githubusercontent.com/IBM/watson-machine-learning-samples/master/cloud/data/electricity/'


if not os.path.isfile(filename): wget.download(base_url + filename)

Visualize the data

import plotly.express as px
import pandas as pd

df = pd.read_csv(filename)
fig = px.line(df, x='date', y=df.columns)
fig.show()

Create connection

conn_meta_props= {
    client.connections.ConfigurationMetaNames.NAME: f"Connection to Database - {datasource_name} ",
    client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: client.connections.get_datasource_type_uid_by_name(datasource_name),
    client.connections.ConfigurationMetaNames.DESCRIPTION: "Connection to external Database",
    client.connections.ConfigurationMetaNames.PROPERTIES: {
        'bucket': bucketname,
        'access_key': cos_credentials['credentials']['editor']['access_key_id'],
        'secret_key': cos_credentials['credentials']['editor']['secret_access_key'],
        'iam_url': 'https://iam.cloud.ibm.com/identity/token',
        'url': cos_credentials['endpoint_url']
    }
}

conn_details = client.connections.create(meta_props=conn_meta_props)

Creating connections...
SUCCESS

Note: The above connection can be initialized alternatively with api_key and resource_instance_id.
The above cell can be replaced with:

conn_meta_props= {
    client.connections.ConfigurationMetaNames.NAME: f"Connection to Database - {db_name} ",
    client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: client.connections.get_datasource_type_uid_by_name(db_name),
    client.connections.ConfigurationMetaNames.DESCRIPTION: "Connection to external Database",
    client.connections.ConfigurationMetaNames.PROPERTIES: {
        'bucket': bucket_name,
        'api_key': cos_credentials['apikey'],
        'resource_instance_id': cos_credentials['resource_instance_id'],
        'iam_url': 'https://iam.cloud.ibm.com/identity/token',
        'url': 'https://s3.us.cloud-object-storage.appdomain.cloud'
    }
}

conn_details = client.connections.create(meta_props=conn_meta_props)

connection_id = client.connections.get_uid(conn_details)

Training data connection

The code in next cell defines connections to created assets.

from ibm_watson_machine_learning.helpers import DataConnection, S3Location

data_connections = []
data_connection = DataConnection(
        connection_asset_id=connection_id,
        location=S3Location(bucket=cos_credentials['bucket_name'],
                            path=filename)
    )

data_connection.set_client(client)
data_connection.write(data=filename, remote_name=filename)
data_connections.append(data_connection)

3. Optimizer definition

Optimizer configuration

Provide the input information for AutoAI optimizer:

• name - experiment name
• prediction_type - type of the problem
• timestamp_column_name - date&time column name/index
• feature_columns - names/indices of supporting feature columns
• pipeline_types - types of the pipelines

from ibm_watson_machine_learning.experiment import AutoAI
from ibm_watson_machine_learning.utils.autoai.enums import TimeseriesAnomalyPredictionPipelineTypes


experiment = AutoAI(wml_credentials, space_id=space_id)

pipeline_optimizer = experiment.optimizer(
            name="Electricity usage anomalies prediction",
            prediction_type=AutoAI.PredictionType.TIMESERIES_ANOMALY_PREDICTION,
            timestamp_column_name='date',
            feature_columns=['industry_a_usage'],
            pipeline_types=[TimeseriesAnomalyPredictionPipelineTypes.PointwiseBoundedBATS,
                           TimeseriesAnomalyPredictionPipelineTypes.PointwiseBoundedBATSForceUpdate,
                           TimeseriesAnomalyPredictionPipelineTypes.PointwiseBoundedHoltWintersAdditive,
                           TimeseriesAnomalyPredictionPipelineTypes.WindowLOF,
                           TimeseriesAnomalyPredictionPipelineTypes.WindowNN,
                           TimeseriesAnomalyPredictionPipelineTypes.WindowPCA]
)

Configuration parameters can be retrieved via pipeline_optimizer.get_params().

4. Experiment run

Call the fit() method to trigger the AutoAI experiment. You can either use interactive mode (synchronous job) or background mode (asychronous job) by specifying background_model=True.

fit_details = pipeline_optimizer.fit(training_data_reference=data_connections, background_mode=False)

Training job fab41e99-dc51-480c-b5d8-f9b5793163e7 completed: 100%|████████| [01:56<00:00,  1.17s/it]

You can use the get_run_status() method to monitor AutoAI jobs in background mode.

pipeline_optimizer.get_run_status()

'completed'

5. Pipelines comparison

You can list trained pipelines and evaluation metrics information in the form of a Pandas DataFrame by calling the summary() method. You can use the DataFrame to compare all discovered pipelines and select the one you like for further deployment.

summary = pipeline_optimizer.summary()
summary

	Enhancements	Estimator	Winner	aggregated_score	average_precision_LevelShift	average_precision_LocalizedExtreme	average_precision_Trend	average_precision_Variance	f1_LevelShift	f1_LocalizedExtreme	...	precision_Trend	precision_Variance	recall_LevelShift	recall_LocalizedExtreme	recall_Trend	recall_Variance	roc_auc_LevelShift	roc_auc_LocalizedExtreme	roc_auc_Trend	roc_auc_Variance
Pipeline Name
Pipeline_4	HPO, FE	Window	True	14.0	1.000000	0.070139	0.403380	0.869842	0.834432	0.166667	...	0.666667	1.000000	0.716667	0.333333	1.00	0.846154	1.000000	0.634181	0.923097	0.965492
Pipeline_1	HPO, FE	Forecasting	True	11.5	1.000000	0.052783	0.818322	0.569901	0.532218	0.023810	...	0.385397	0.280672	1.000000	0.166667	1.00	0.538462	1.000000	0.622881	0.990741	0.728253
Pipeline_5	HPO, FE	Window	True	10.0	0.704441	0.081628	0.383347	0.845679	0.775714	0.058061	...	0.472710	0.380410	0.637500	0.833333	0.75	0.974359	0.778339	0.750000	0.913580	0.955188

3 rows × 24 columns

Check pipeline details by calling get_pipeline_details(). By default details of best pipeline are returned.

best_pipeline_name = summary[summary.Winner==True].index.values[0]
print('Best pipeline is:', best_pipeline_name)

pipeline_details = pipeline_optimizer.get_pipeline_details()
pipeline_details

Best pipeline is: Pipeline_4

{'composition_steps': ['Flatten'],
 'pipeline_nodes': ['Flatten', 'Window'],
 'tsad_metrics':                                                      score
 holdout  {'agg': {'average_precision': {'LevelShift': {...}

import json
# Check the metrics details
print(json.dumps(pipeline_details["tsad_metrics"].iloc[0,0], indent=4, sort_keys=True))

{
    "agg": {
        "average_precision": {
            "LevelShift": {
                "mean": 1.0,
                "range": [
                    1.0,
                    1.0
                ]
            },
            "LocalizedExtreme": {
                "mean": 0.07013888888888889,
                "range": [
                    0.041666666666666664,
                    0.11666666666666667
                ]
            },
            "Trend": {
                "mean": 0.40337961163554553,
                "range": [
                    0.3932949854582981,
                    0.4129651641358298
                ]
            },
            "Variance": {
                "mean": 0.8698422582327617,
                "range": [
                    0.7908484228322286,
                    0.9819004524886878
                ]
            }
        },
        "f1": {
            "LevelShift": {
                "mean": 0.8344322344322345,
                "range": [
                    0.8,
                    0.8571428571428571
                ]
            },
            "LocalizedExtreme": {
                "mean": 0.16666666666666666,
                "range": [
                    0.0,
                    0.25
                ]
            },
            "Trend": {
                "mean": 0.8000000000000002,
                "range": [
                    0.8,
                    0.8
                ]
            },
            "Variance": {
                "mean": 0.9127272727272727,
                "range": [
                    0.8181818181818181,
                    0.9600000000000001
                ]
            }
        },
        "precision": {
            "LevelShift": {
                "mean": 1.0,
                "range": [
                    1.0,
                    1.0
                ]
            },
            "LocalizedExtreme": {
                "mean": 0.1111111111111111,
                "range": [
                    0.0,
                    0.16666666666666666
                ]
            },
            "Trend": {
                "mean": 0.6666666666666666,
                "range": [
                    0.6666666666666666,
                    0.6666666666666666
                ]
            },
            "Variance": {
                "mean": 1.0,
                "range": [
                    1.0,
                    1.0
                ]
            }
        },
        "recall": {
            "LevelShift": {
                "mean": 0.7166666666666667,
                "range": [
                    0.6666666666666666,
                    0.75
                ]
            },
            "LocalizedExtreme": {
                "mean": 0.3333333333333333,
                "range": [
                    0.0,
                    0.5
                ]
            },
            "Trend": {
                "mean": 1.0,
                "range": [
                    1.0,
                    1.0
                ]
            },
            "Variance": {
                "mean": 0.8461538461538461,
                "range": [
                    0.6923076923076923,
                    0.9230769230769231
                ]
            }
        },
        "roc_auc": {
            "LevelShift": {
                "mean": 1.0,
                "range": [
                    1.0,
                    1.0
                ]
            },
            "LocalizedExtreme": {
                "mean": 0.634180790960452,
                "range": [
                    0.4406779661016949,
                    0.9067796610169491
                ]
            },
            "Trend": {
                "mean": 0.9230967078189302,
                "range": [
                    0.9197530864197531,
                    0.9259259259259259
                ]
            },
            "Variance": {
                "mean": 0.96549245147376,
                "range": [
                    0.9252336448598131,
                    0.9971243709561467
                ]
            }
        }
    },
    "aggregated_score": [
        {
            "P4": 14.0
        },
        {
            "P1": 11.5
        },
        {
            "P5": 10.0
        },
        {
            "P2": 9.5
        },
        {
            "P6": 8.0
        },
        {
            "P3": 7.0
        }
    ],
    "iterations": [
        {
            "average_precision": {
                "LevelShift": 1.0,
                "LocalizedExtreme": 0.11666666666666667,
                "Trend": 0.4038786853125088,
                "Variance": 0.8367778993773689
            },
            "f1": {
                "LevelShift": 0.8571428571428571,
                "LocalizedExtreme": 0.25,
                "Trend": 0.8,
                "Variance": 0.9600000000000001
            },
            "precision": {
                "LevelShift": 1.0,
                "LocalizedExtreme": 0.16666666666666666,
                "Trend": 0.6666666666666666,
                "Variance": 1.0
            },
            "recall": {
                "LevelShift": 0.75,
                "LocalizedExtreme": 0.5,
                "Trend": 1.0,
                "Variance": 0.9230769230769231
            },
            "roc_auc": {
                "LevelShift": 1.0,
                "LocalizedExtreme": 0.9067796610169491,
                "Trend": 0.9236111111111112,
                "Variance": 0.9741193386053199
            }
        },
        {
            "average_precision": {
                "LevelShift": 1.0,
                "LocalizedExtreme": 0.05208333333333333,
                "Trend": 0.4129651641358298,
                "Variance": 0.9819004524886878
            },
            "f1": {
                "LevelShift": 0.846153846153846,
                "LocalizedExtreme": 0.25,
                "Trend": 0.8,
                "Variance": 0.9600000000000001
            },
            "precision": {
                "LevelShift": 1.0,
                "LocalizedExtreme": 0.16666666666666666,
                "Trend": 0.6666666666666666,
                "Variance": 1.0
            },
            "recall": {
                "LevelShift": 0.7333333333333333,
                "LocalizedExtreme": 0.5,
                "Trend": 1.0,
                "Variance": 0.9230769230769231
            },
            "roc_auc": {
                "LevelShift": 1.0,
                "LocalizedExtreme": 0.5550847457627119,
                "Trend": 0.9259259259259259,
                "Variance": 0.9971243709561467
            }
        },
        {
            "average_precision": {
                "LevelShift": 1.0,
                "LocalizedExtreme": 0.041666666666666664,
                "Trend": 0.3932949854582981,
                "Variance": 0.7908484228322286
            },
            "f1": {
                "LevelShift": 0.8,
                "LocalizedExtreme": 0.0,
                "Trend": 0.8,
                "Variance": 0.8181818181818181
            },
            "precision": {
                "LevelShift": 1.0,
                "LocalizedExtreme": 0.0,
                "Trend": 0.6666666666666666,
                "Variance": 1.0
            },
            "recall": {
                "LevelShift": 0.6666666666666666,
                "LocalizedExtreme": 0.0,
                "Trend": 1.0,
                "Variance": 0.6923076923076923
            },
            "roc_auc": {
                "LevelShift": 1.0,
                "LocalizedExtreme": 0.4406779661016949,
                "Trend": 0.9197530864197531,
                "Variance": 0.9252336448598131
            }
        }
    ],
    "supporting_rank": {
        "average_precision": {
            "LevelShift": {
                "P1": 2.0,
                "P2": 2.0,
                "P3": 6.0,
                "P4": 2.0,
                "P5": 4.0,
                "P6": 5.0
            },
            "LocalizedExtreme": {
                "P1": 4.0,
                "P2": 5.0,
                "P3": 1.0,
                "P4": 3.0,
                "P5": 2.0,
                "P6": 6.0
            },
            "Trend": {
                "P1": 1.5,
                "P2": 1.5,
                "P3": 6.0,
                "P4": 4.0,
                "P5": 5.0,
                "P6": 3.0
            },
            "Variance": {
                "P1": 5.0,
                "P2": 6.0,
                "P3": 4.0,
                "P4": 1.0,
                "P5": 3.0,
                "P6": 2.0
            }
        }
    }
}

6. Deploy and Score

In this section you will learn how to deploy and score pipeline model as online deployment using WML instance.

Online deployment creation

from ibm_watson_machine_learning.deployment import WebService

service = WebService(wml_credentials, source_space_id=space_id)

service.create(
    experiment_run_id=pipeline_optimizer.get_run_details()['metadata']['id'],
    model=best_pipeline_name, 
    deployment_name="Electricity usage anomalies prediction"
    )

Preparing an AutoAI Deployment...
Published model uid: 1cb20b02-e5e3-4561-b212-32ca8b93dad8
Deploying model 1cb20b02-e5e3-4561-b212-32ca8b93dad8 using V4 client.


#######################################################################################

Synchronous deployment creation for uid: '1cb20b02-e5e3-4561-b212-32ca8b93dad8' started

#######################################################################################


initializing
Note: online_url is deprecated and will be removed in a future release. Use serving_urls instead.
..
ready


------------------------------------------------------------------------------------------------
Successfully finished deployment creation, deployment_uid='bb7651a8-f386-493f-aa60-c91338bc4600'
------------------------------------------------------------------------------------------------

To show all available information about the deployment use the .get_params() method:

service.get_params()

Note: online_url is deprecated and will be removed in a future release. Use serving_urls instead.

{'entity': {'asset': {'id': '1cb20b02-e5e3-4561-b212-32ca8b93dad8'},
  'custom': {},
  'deployed_asset_type': 'model',
  'hybrid_pipeline_hardware_specs': [{'hardware_spec': {'name': 'S',
     'num_nodes': 1},
    'node_runtime_id': 'auto_ai.tsad'}],
  'name': 'Electricity usage anomalies prediction',
  'online': {},
  'space_id': '3af17fad-fc7b-4507-ad3f-26c7de6479d8',
  'status': {'online_url': {'url': 'https://yp-qa.ml.cloud.ibm.com/ml/v4/deployments/bb7651a8-f386-493f-aa60-c91338bc4600/predictions'},
   'serving_urls': ['https://yp-qa.ml.cloud.ibm.com/ml/v4/deployments/bb7651a8-f386-493f-aa60-c91338bc4600/predictions'],
   'state': 'ready'}},
 'metadata': {'created_at': '2023-04-04T13:36:40.623Z',
  'id': 'bb7651a8-f386-493f-aa60-c91338bc4600',
  'modified_at': '2023-04-04T13:36:40.623Z',
  'name': 'Electricity usage anomalies prediction',
  'owner': 'IBMid-270005A11W',
  'space_id': '3af17fad-fc7b-4507-ad3f-26c7de6479d8'},
 'system': {'warnings': [{'id': 'Deprecated',
    'message': 'online_url is deprecated and will be removed in a future release. Use serving_urls instead.'}]}}

Scoring

You can use the score method to get predictions for defined forecasting window. You can either send payload records or use empty list.

filename = 'electricity_usage_new.csv'
if not os.path.isfile(filename): wget.download(base_url + filename)
newData= pd.read_csv(filename)    
predictions = service.score(newData.drop(columns=['date', 'label'], axis=1))
predictions

{'predictions': [{'fields': ['prediction'],
   'values': [[[1]],
    [[1]],
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    [[1]],
    [[1]],
    [[1]],
    [[1]],
    [[1]],
    [[1]],
    [[1]],
    [[1]],
    [[1]],
    [[1]],
    [[1]],
    [[1]],
    [[1]]]}]}

Visualization of predictions

prediction_values = [pred[0][0] for pred in  predictions['predictions'][0]['values']]
pred_df = pd.DataFrame({'date' : pd.to_datetime(newData['date']),
                        'industry_a_usage' : newData['industry_a_usage'],
                        'label': newData['label'],
                        'predictions' : prediction_values})

fig = px.line(pred_df, x='date', y=['industry_a_usage'])

x_labels = pred_df[(pred_df['label'] == -1)]['date']
y_labels = pred_df[(pred_df['label'] == -1)]['industry_a_usage']
fig.add_scatter(x=x_labels, y=y_labels, mode='markers', marker=dict(color='green', symbol='x',size=10))


x_predictions = pred_df[pred_df['predictions'] == -1]['date']
y_predictions = pred_df[pred_df['predictions'] == -1]['industry_a_usage']
fig.add_scatter(x=x_predictions, y=y_predictions,mode='markers', marker=dict(color='red', symbol='circle-dot'))

fig.data[1].name = "anomalies"
fig.data[2].name = "predicted_as_anomalies"
fig.show()

7. Clean up

If you want to clean up all created assets:

• experiments
• trainings
• pipelines
• model definitions
• models
• functions
• deployments

please follow up this sample notebook.

8. Summary and next steps

You successfully completed this notebook!.

You learned how to use ibm-watson-machine-learning to run AutoAI experiments.

Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Authors

Jun Wang, is a Software Architect and Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increases clients' ability to turn data into actionable knowledge.

Copyright © 2023 IBM. This notebook and its source code are released under the terms of the MIT License.